A number of techniques are known to those of skill in the art for providing user reviews on social review systems. The advent of the World Wide Web (“Web”) has created a widespread phenomenon of such social review systems, which are essentially systems that support the reviews of particular collections of items by large, self-selected groups of non-professional reviewers. Several types of systems existed prior to the establishment of the Web as a consumer platform, such as Zagat for reviewing restaurants and Consumer Reports for reviewing products. These systems often employed cumbersome mail forms, questionnaires, or phone surveys, and the results were typically edited by professional editors. By contrast, today there are numerous online social review systems that help organize and share socially produced information valuable to assist in making purchasing decisions, choosing a movie (e.g., Yahoo! Movies), choosing services and shops, renting a DVD, buying a book, or booking travel arrangements. Such systems typically include a given collection of items, such as, books, movies, or restaurants, as well as a collection of ratings, accompanied by reviews provided by users of the system. For example, on Yahoo! Movies, a recent relatively obscure movie, La Vie en Rose, released on Jun. 8, 2007, maintained a total of 573 ratings accompanied by 89 written reviews. A more popular film, Ratatouille, attained 21,004 ratings accompanied by 1,743 user reviews, over a 6 week period. Additionally, some systems keep separate collections for professional and user-based reviews.
The prior art systems generally provide aggregation of user ratings, for example, an overall average user rating, or a ranking of a given item (book, movie, hotel, or restaurant, etc.) among a group of items. Some systems allow for multi-dimensional ratings or inputs (e.g., Zagat incorporates ratings of restaurants by quality of food, décor, service, and cost, while Yahoo! Movies incorporates ratings by story, acting, direction, and visuals) with aggregation of ratings along each dimension. Other systems provide a collaborative filtering mechanism, whereby background information for a user and prior ratings of items in a collection are stored and utilized to make future rating predictions for other users of the system.
Traditional social review systems are limited, however, in that they provide inadequate support for understanding and evaluating the numerous ratings and reviews entered by reviewers and users. For example, some reviewers are inconsistent, some have particular biases, and some have no appropriate frame of reference. Although some systems provide a measure of “usefulness” for a given review (e.g., “6 of 11 people found this review helpful”), or some other characteristic (e.g., allowing users to rate a review as useful, funny, or cool), these are merely simple aggregations of existing votes. Other systems provide for a trust system to rate reviewers, but this is a one-dimensional approach.
The ability of a user to interpret an opinion of a given reviewer is crucial to making a good decision. A user should interpret or weight a restaurant review that comes from a reviewer of discerning taste and familiarity with the relevant cuisine differently than a similar review coming for a random Web surfer that happened to wander into the restaurant. To mitigate this problem, most popular systems attempt to characterize reviewers, but this is limited in most cases to the total number of reviews written by the given reviewer. Even with the knowledge that the reviewer has written many previous reviews, it is still not a simplistic task to arrive at an informed decision. The ability for any individual to enter a review also exacerbates this problem.
To overcome shortcomings and problems associated with existing systems and methods for providing context for user reviews, embodiments of the present invention provide systems and methods for ranking and annotating reviews with inferred analytics, including reviews personalized to prior user experience.